Control beliefs moderate emotion influences on complex ... and... · Control beliefs moderate...

COGNITION AND EMOTION, 2003 (under review)

Control beliefs moderate emotion influences

on complex problem solving

Miriam SperingRuprecht-Karls-Universität Heidelberg, Germany

Dietrich WagenerUniversität Mannheim, Germany

Joachim FunkeRuprecht-Karls-Universität Heidelberg, Germany

The assumption that positive affect leads to a better performance in various cognitive tasks hasbecome well established. We investigated whether positive and negative feedback-inducedemotions influence performance and strategies of 74 university students in a computer-simulated complex problem-solving scenario. We further analysed whether control beliefsmoderate the relation between emotions and complex problem solving. While overall scenarioperformance was not affected by emotions, strategy was: Participants with negative emotionswere more information-oriented in their problem-solving behaviour. Control beliefssignificantly interacted with induced emotions: Students with internal control beliefs benefitedmost from positive emotions, whereas students with external control beliefs performed bestwhen no emotions were induced. We suggest that the moderating effect may be due to amotivating side effect of the emotion elicitation and outline different approaches to test emotioninfluences in complex problem solving.

INTRODUCTION

While mathematician Andrew Wiles was brooding over Fermat’s last theorem – a

complex problem that had exasperated dozens of illustrious mathematicians before – he was in

a state of emotional agitation. In 1993, after living in seclusion for seven years, his first

thorough attempt of proof was heavily criticised. It was within this episode of distress and

depression that he brought about a cast-iron proof of Fermat’s last theorem (Singh, 1997). This

Emotions and complex problem solving 2

excerpt from the stirring history of Fermat’s last theorem shows that feelings can influence our

thinking in an either beneficial or harmful way. However, anecdotes of that kind confront us

with numerous questions: What emotions are beneficial to our problem-solving abilities and

what emotions impair our performance? In what tasks do emotions generally exert an influence

and why?

The present study addressed the effects of emotions on problem solving in complex and

dynamic situations. In recent years, there has been growing research interest in the role of affect

in cognitive processes (e.g., Fiedler, 2001; Schwarz, 2000). Yet, most empirical studies in this

field have focussed solely on unspecific positive or negative mood states and have employed

simple cognitive tasks rather than more complex problem-solving situations. With the current

investigation we pursued three goals. Firstly, we explored whether it is possible to induce

emotions in individuals who solve complex problems. Secondly, we tested whether the findings

from studies on the influence of affect on simple cognitive tasks apply to complex problems.

Since we expected that control beliefs play a distinct intervening role in emotion regulation, we

thirdly examined whether control beliefs moderate the influence of emotions on complex

problem solving. In accordance with these objectives, we clarify the three main issues of this

investigation in the following paragraphs and summarise our ideas in a schematic model.

THEORETICAL RATIONALE AND RESEARCH OVERVIEW

Complex problem solving: When you do not know what to do

As there is no unified definition of complex problem solving (CPS), the concept is often

circumscribed by contrasting it with simple cognitive tasks in terms of the following five

characteristic criteria: (a) complexity of the situation, (b) connectivity of variables, (c) dynamic

development, (d) intransparency or opaqueness, and (e) polytely (pursue of multiple goals; see

Frensch & Funke, 1995, for a detailed consideration).

Simple problem-solving tasks, e.g., Duncker's (1945) candle task, Maier's (1930) nine-

dot problem, or the famous Tower of Hanoi (e.g., Simon, 1975), require creative ingenuity and

restructuring of the given information. However, these tasks do not directly reflect the demands

of problems in complex and dynamic situations that people are confronted with in their daily

professional and private lives. In response to this criticism a new approach was initiated by

Broadbent (e.g., Broadbent, 1977) and Dörner (e.g., Dörner, 1980). Following this pioneering


work, research on CPS has made considerable headway in the last 30 years and has been

studied extensively by means of computer-simulated scenarios, like Lohhausen, Tailorshop, or

Biology Lab (see Funke, 2001, for an overview). These complex and semantically-rich

computerised tasks are constructed to mirror characteristics of real-life problems (e.g., Berry &

Broadbent, 1995; Brehmer, 1995; Dörner & Wearing, 1995). In the arguably best-known

scenario Lohhausen (Dörner, Kreuzig, Reither, & Stäudel, 1983), participants act as mayor of a

small town and have to direct the economic climate and people’s well-being to govern the town

successfully. These complex problem-solving tasks demand the gain and integration of

information, the elaboration and attainment of goals, the planning of action, decision-making,

and self-management (Dörner, 1986).

Up to now, most research on CPS centred around one of the three areas (a) the influence

of personal factors (e.g., intelligence, previous knowledge, or personality traits) on CPS, (b) the

influence of situational determinants (e.g., group problem solving, conceptual formulation, or

feedback) on CPS, and (c) system characteristics (e.g., formal aspects such as the semantic

embedding of the system). Despite considerable progress made in this research domain, the

question of whether affect (distinct emotions or moods) influences CPS has rarely been

addressed.

Emotions: When you know where your feelings come from

Looking at affective influences on cognition, it appears important to distinguish between

emotions and moods (e.g., Siemer, 2001). Emotions are commonly understood as short-lived,

intense phenomena that usually have a clear cognitive content and a salient cause that is highly

accessible to the person experiencing the emotion (e.g., Clore, Schwarz, & Conway, 1994).

Moods can be differentiated from emotions by their core properties (a) globality, (b)

diffuseness, (c) lack of intentionality, (d) longer duration, and (e) lower intensity (e.g., Clore et

al., 1994; Ekman, 1994; Frijda, 1994; Oatley & Johnson-Laird, 1987, 1996; Schwarz, 1990). As

described earlier, most studies to date have, by and large, focussed on the influence of moods

on cognitive processes or have only dealt with simple cognitive tasks. We summarise the results

of these studies and outline those investigations dealing with the influence of person variables

on complex problem-solving tasks. It should be noted that the terms affect, emotion, and mood

have been used interchangeably in most previous studies. Henceforth, we will employ these

terms in accord with the cited author.


Emotions and complex problem solving

A number of studies looked at the role of constructs such as perceived emotional

intelligence, emotional resilience, emotional reactivity, uncertainty, and anxiety as a trait in

complex problem-solving scenarios (Dörner, 1998; Stäudel, 1987). Likewise, other authors

have dealt with the influence of stress, coping abilities, and achievement motivation on

complex problem solving (Hesse, Spies, & Lüer, 1983). The results of these studies are

inconsistent: Whereas some authors have found emotional variables to impair complex problem

solving, most studies detected no effect at all. However, all studies have focussed almost solely

on the outcome of complex problem-solving tasks and disregarded the conceivable influence of

emotional variables on processing strategies. Moreover, emotional variables have not been

induced experimentally.

As previous research demonstrates, moods and emotions can profoundly influence both

strategic approaches and solution quality of cognitive tasks in general. Some of the uncertainty

surrounding the question of whether this influence is disruptive or facilitative seems now

resolved: The results of most studies, employing a wide range of induction methods and

cognitive tasks, indicate that positive affect enhances simple and creative problem solving (see

Isen, Daubman, & Nowicki, 1987). Positive affect also facilitates efficient decision making in

complex environments (e.g., in making a medical diagnosis; Isen, 2001). There is further

evidence for the contention that elated moods lead to flexible, creative, efficient, and thorough

thinking and that people prefer wider cognitive categories when induced with positive affect

(Bohner, Marz, Bless, Schwarz, & Strack, 1992; Fiedler, 2001; Forgas & Fiedler, 1996; Isen,

2001). In addition to that, affective states are assumed to influence the selection of strategies for

information processing in simple cognitive tasks. A large body of experimental research

documents that individuals in a positive affective state are more likely to use a heuristic

processing strategy (characterised by top-down processing and the productive use of generic

knowledge structures). Negative affect, on the other hand, facilitates careful bottom-up

processing and a more systematic gathering of information. Individuals in a bad mood pay more

attention to the details at hand (Fiedler, 2001; Hertel, Neuhof, Theuer, & Kerr, 2000; Schwarz,

2000).

Theoretical explanations. Different descriptive models have been offered for these

findings, as for instance the affect-infusion model (Forgas, 1994), the mood-and-general-


knowledge approach (Bless & Fiedler, 1995), or the affect-as-information hypothesis (Schwarz,

1990). Fiedler’s affect-cognition theory (2001) plausibly describes the underlying cognitive

processes and postulates that positive and negative mood states are affective cues to appetitive

and aversive settings: “While negative mood supports the conservative function of sticking to

the stimulus facts and avoiding mistakes, positive mood supports the creative function of active

generation, or enriching the stimulus input with inferences based on prior knowledge” (Fiedler,

2001, p. 3). Fiedler uses the Piagetian terms accommodation (tuning the cognitive system to fit

the stimulus environment) and assimilation (transforming external environment to fit internal

knowledge structures), to label these two processes, respectively. Negative mood thus

facilitates accommodation, and positive mood supports assimilative functions.

Control beliefs: When you either believe in yourself or trust in chance or others

CPS is not only thought to be influenced by emotional states but also by personality

factors, especially control beliefs. Generalised control beliefs constitute relatively stable

personality characteristics, reflecting an individual’s belief that he or she can cope with difficult

demands. This construct has been operationalised under a variety of different labels, namely

self-efficacy, optimistic self-belief, locus of control of reinforcement, and hope. According to

Krampen’s (1991) expectancy-value approach, control beliefs are theoretically derived from the

expectancy of contingency in action and represent the subjective expectancy that feasible

actions will be successful in any given situation. Similar to the notion of internal versus

external locus of control (LOC), Krampen conceives internal control beliefs as pointing to a

person’s opinion that he or she is in a position to influence events in any given situation by his

or her own action. External control beliefs indicate that an individual perceives upcoming

events as controlled by chance or other people’s influence. According to Krampen, people with

internal LOC are characterised by high activity, self-assuredness, inventiveness, quick decision-

making, and high responsibility. These individuals trust in their own abilities and feel relatively

independent of other persons or situations. People with external LOC, on the other hand, are

more insecure and passive when facing new situations. They can be distinguished by their low

self-assuredness and they feel dependent on other people or trust in chance and fate.

Control beliefs and complex problem solving

Previous studies on the relationship between personality factors and CPS have merely

concentrated on cognitive styles, e.g., impulsivity vs. reflexivity, strategic flexibility, or


heuristic competence, and on dispositional personality traits, such as extraversion, neuroticism,

and rigidity (Dörner et al., 1983; see Schaub, 2001, for an overview). The studies yielded a

variety of singular findings that cannot be fitted into a coherent pattern. Stäudel (1987) has

found positive correlations between problem-solving ability and self-assuredness. According to

Krampen (1991), self-assuredness is a component of internality in control beliefs and a relation

between control beliefs and CPS is therefore feasible. There are two ways in which control

beliefs may influence CPS: In addition to a direct effect, control beliefs could moderate the

relationship between emotions and CPS, as Dörner (1998) suggests that control beliefs affect

CPS by influencing the regulation of emotions (see also Bartl & Dörner, 1998). Individuals

with internality and externality in control beliefs should deal differently with emotions: We

expected that individuals with an internal LOC benefit more from positive emotions than do

individuals with an external LOC. Individuals with an external LOC should, however, be better

able to regulate negative emotions.

Figure 1. Schematic depiction of the interplay between emotions, control beliefs, complex problem-solving performance, and complex problem-solving behaviour. Arrows with labels H1 to H4 stand forthose hypotheses that were tested here.

The theoretical assumptions described so far are summarised in Figure 1. We provide

this as a heuristic framework to illustrate the theoretical context into which the current study is

incorporated. The framework depicts the origins and functions of emotions in a feedback loop.

Concerning the origin of emotions, a sensed value (evaluation) is compared to a reference value


(goal) that is set in accordance with control beliefs. If there is a positive discrepancy, positive

emotions arise; if the discrepancy is in fact negative, negative emotions are triggered (Carver &

Scheier, 1999). Those connections referring to the origin of emotions were not tested here. The

illustration shows the pertinent relations between control beliefs (as a trait), emotions, complex

problem-solving behaviour (action), and performance (outcome).

OVERVIEW OF THE PRESENT STUDY

We report and discuss the findings of an experimental study on the influence of

feedback-induced emotions and control beliefs as a trait on CPS. A computer-simulated

scenario was used to test complex problem-solving performance and problem-solving

behaviour. Four hypotheses were tested. Firstly, it is predicted that positive emotions, in

contrast to negative emotions, lead to a better performance in the complex problem-solving

scenario employed, as this task requires assimilative functions (Hypothesis 1). Secondly,

positive and negative emotions are predicted to lead to different problem-solving behaviours:

Positive emotions cause an intuitive, hypothesis-oriented approach, whereas negative emotions

are associated with more detail-oriented, information-based action (Hypothesis 2). Thirdly,

control beliefs are hypothesised to directly influence CPS. Individuals with internal LOC are

predicted to show a significantly higher problem-solving performance as compared to

participants with external LOC (Hypothesis 3). Finally, it is expected that control beliefs

moderate the influence of emotions on CPS. Individuals, who have an internal LOC, benefit

from positive emotions more than individuals with an external LOC. Likewise, individuals,

who are external in their control beliefs, perform better than individuals with internality in their

control beliefs when experiencing negative emotions (Hypothesis 4). Gender differences

(effects of biological sex) were also taken into account as an additional factor for each of the

hypotheses under study.

METHODS

Measures

Emotion induction. A wide variety of induction techniques have been employed in

emotion research amongst which music and films have been most successful (e.g., Niedenthal,


Halberstadt, & Setterlund, 1997; Westermann, Spies, Stahl, & Hesse, 1996). In selecting an

adequate treatment method for this study, two constraints had to be taken into account. Firstly,

the treatment had to be administered between the introductory period and the testing period of

the computer-simulated scenario and was hence restricted to a very short time frame. Secondly,

the emotion induction method had to be relevant to the problem-solving task to appear

authentic, which made music and films not useful as induction material. Positive and negative

emotions were instead elicited experimentally two times throughout the experiment by giving

false (either positive or negative) feedback on performance. The first feedback was

administered by telling students a false score after they had completed the spatial-reasoning

test, a subtest of the intelligence structure test 2000 (Intelligenz-Struktur-Test, I-S-T 2000;

Amthauer, Brocke, Liepmann, & Beauducel, 1999). The second feedback was provided after

half time of the computer-simulated scenario and was displayed automatically in a pop-up

window, which informed participants about their position in a fictitious ranking (positive: “You

are in position 12 out of 250 and are thus better than 95,2% of all participants”, negative: “You

are in position 208 out of 250. That means that 83,2% of all participants have performed better

than you”).

Emotion measures. Emotions were measured seven times using a 14-item questionnaire.

Participants were asked to mark a 10-cm line between the two poles null and all pervasive for

each of 14 emotion adjectives (content, sad, excited, tense, confused, angry, anxious, surprised,

enthusiastic, interested, calm, happy, ashamed, proud). Emotion labels were put in different

random order on each of the questionnaires to prevent sequence effects. This selection of

adjectives was based on a questionnaire by Schmidt-Atzert and Hüppe (1996) and on a German

version of Watson et al.’s PANAS scales (Krohne, Egloff, Kohlmann, & Tausch, 1996). The

questionnaire was a paper and pencil test and was announced by a pop-up window inside the

computer-simulated scenario.

Control beliefs. Krampen’s (1991) competence and control beliefs questionnaire

(Fragebogen zu Kompetenz- und Kontrollüberzeugungen, FKK) has been used frequently in a

variety of contexts since its first version was published in German (Krampen, 1981). The

questionnaire measures three aspects of LOC and one aspect of competence orientation, namely

internality, powerful others externality, chance control externality, and self-concept on four

distinguishable scales. These four scales can be aggregated to two secondary scales and a


tertiary scale (internality vs. externality in control beliefs) that will be used for purposes of data

analysis in this study. Krampen (1991) has argued that control beliefs can be domain-specific.

We therefore rephrased some of the items of the FKK, thus yielding a questionnaire measuring

control beliefs in problem solving, the FKK-PS. Table 1 shows four items and adjunctive scales

with indication of their reliability.

Table 1

Overview of FKK-PS scales and text examples with reliability indexes

Scale (with number of items) Item (with item-number) Cronbach’s alphaPrimary scales

„Whether I am able to solve a problemor not totally depends on my own effortand endurance.” (5)

.60

„Whether I am able to solve a problemor not depends on the support of otherpeople.” (26)

.74

„Whether I am able to solve a problemor not is entirely a matter of luck.” (15)

.72

Internality incontrol beliefs (8)

Social externality in controlbeliefs (8)

Chance control (8)

Self-concept of ownabilities (8)

„I sometimes do not have a clue what todo under the circumstances.“ (24)a

.78

Secondary scales.80Self-efficacy (16)

Externality in control beliefs (16) .78Tertiary scaleInternality versus externality incontrol beliefs (32)

.63

a Item is reverse coded.

Complex problem solving. The computer-simulated scenario FSYS 2.0 (Wagener, 2001)

was used in the current study1. In this dynamic and complex system, problem solvers have to

manage a forest enterprise over a period of 50 fictitious months (simulation cycles) at a profit.

In order to achieve that goal, they have to plant, raise, and fell trees in five structurally

equivalent partitions of the forest and have to maintain the wood’s quality (e.g., fertilise

grounds, do pest control). FSYS is based on Dörner’s (1986) model of demands on problem

solvers. The scenario is accordingly designed to indicate strategies in different aspects of

problem-solving behaviour additionally to the overall success in controlling the system. FSYS

delivers 14 scales that are depicted in Table 2. The scales are organised in four groups,


representing scenario performance (SP), quality of measures (QM), acquisition of information

(AI) and self-management (SM). For the 90-minute duration of the test session, the program

protocols and calculates the individual’s performance data with regard to the overall profit

yielded and behavioural components on the scales mentioned before.

Table 2

Overview of selected FSYS scales

Dimension and scale name Description of scaleScenario performance (SP)Earned capital (SKAPKOR) Account balance (income minus expenses for wood

maintenance) plus value of forest after 50 cyclesQuality of measures (QM)Prevention of errors (MNOFEHL)Assigning of priorities (MPRIORI)Efficiency of actions (MEFFIZI)Early comprehension (MVERSTA)

Correct dosage of e.g., fertiliser and pesticideAdequateness of decisions in goal conflictsTotal vs. partial achievement of subgoalsTime in scenario when all possible measures havebeen ordered once

Acquisition of information (AI)Early orientation (IORIENT)Information before acting(IVORHAND)Reading statistics (IFEEDS)

Surveying of forest areas (IKONTI)

Number of text elements accessed in first five cyclesKnowledge acquisition about effects of measuresbefore actingGaining overview of business process by readinggraphs and/or tablesContinuity of inspection of forest areas

Self-management (SM)Decidedness in action taking(ESICHER)

Number of decisions that have been cancelled withinthe same cycle

Additional measures. Personal data (e.g., age, gender, subject of studies) were collected

at the end of the experiment on a short standardised questionnaire that also asked for feedback

on the experiment and any inconveniences that might have disrupted a smooth procedure. No

such influences were reported.

Participants and procedure

Participants were 74 students and graduates of different fields of study; n = 32 were

female and n = 42 were male; the mean age was M = 24,6 (SD = 2,89). Students were contacted

in academic courses during March 2001 and took part on a voluntary basis. They were either


returned partial credit for a course requirement or got a book-gift. The experiment took place at

the Department of Psychology of the University of Heidelberg.

Participants were sent the modified version of the FKK-PS via email one week before

the experimental session and were asked to return the questionnaire before the session.

According to their result in the FKK-PS (either internality or externality in control beliefs)

respondents were assigned to three conditions (either positive or negative treatment or a control

group without any treatment) in a randomised manner thus resulting in a 2x3 design.

Individuals were distributed equally across the cells with n = 12 subjects in the four treatment

groups and n = 13 subjects in the two control groups. The students were then tested

individually. They first had to complete the spatial-reasoning test from the I-S-T 2000

(Amthauer et al., 1999) and were then given the written instructions for the computer-simulated

scenario. The space-test result was employed to induce emotions by giving false feedback on

the score. Emotions were measured directly before the space-test and after the feedback. People

then had 90 minutes to work on the scenario FSYS. Every tenth cycle, a pop-up window

instructed participants to complete another emotion questionnaire. After the 25th cycle, the

second feedback was given automatically in a pop-up window. Emotions were measured

immediately afterwards. Due to high standardisation of instructions and procedure, there was

no interaction between participants and experimenter except during the first feedback-cycle.

RESULTS

The significance level for all statistical hypotheses tests was set at p = .05; significance

levels for treatment-check purposes could be set at p = .01 since higher effect sizes were

expected here. All significance tests were one-tailed. In addition to levels of significance, effect

sizes d, f, or f2 are reported, following the conventions of Cohen (1987). An a priori power

analysis with G-Power (Erdfelder, Faul, & Buchner, 1996) revealed that, in consideration of a

sample size of N = 74, an expected large effect (f = 0.40) can be detected with a power of 0.85

for Hypotheses 1 and 2 and a medium effect (f2 = 0.15) can be verified with a power of 0.91 for

Hypothesis 3 and with a power of 0.84 for Hypothesis 4. When analyses of variance

(ANOVAs) are reported, different degrees of freedom within the same data analysis reflect the

fact that outliers were omitted.

Manipulation check: How good was the emotion elicitation?


To test whether the induction method was successful, emotion scores were compared

between the three groups for each of the two points of measurement before and after the

feedback was given. Emotion scores were derived from a differential R-technique factor

analysis (Barton, Cattell, & Curran, 1973) that yielded three factors. Equally weighted emotions

were aggregated to obtain three single emotion scores that represent the factors by summing

participants’ ratings of arousal, positive, and negative emotions. Subsequent one-way ANOVAs

were then carried out on these emotion scores with feedback as an independent variable. We

also conducted a multivariate analysis of variance (MANOVA) on emotion score, but because

the outcomes were comparable, we report the results of the ANOVA here. All treatment-group

means differed significantly from those of the control group in the expected direction.

Generally, positive feedback elicited substantially higher positive emotions whereas

negative feedback reduced positive emotions; F(2, 70) = 5.05, p < .01, f = 0.38 for the first

emotion induction, F(2, 71) = 10.03, p < .01, f = 0.53 for the second induction. Negative

emotions increased after negative feedback and decreased after the positive feedback,

respectively, but this effect was only significant for the second induction; F(2, 70) < 1, n.s., f =

0.12, F(2, 68) = 9.74, p < .01, f = 0.54. Treatment effects were not significant at any other point

of measurement after the first and second emotion induction. Concerning arousal, only the

second negative feedback as compared to the other conditions led to a stronger arousal, F(2, 71)

= 5.166, p < .01, f = 0.38.

To address the issue of which emotions were primarily elicited, effect sizes were

compared. The first and second positive induction chiefly elicited medium sized effects on

pride, while the two negative inductions increased anger and shame and led to reduced

contentment. Note that the effect sizes for shame (d = 0.57 and 0.80) and contentment (d = -

1.00 and -1.54) indicate medium to large effects for both, the first and the second induction,

respectively.

Finally, the manipulation check was conducted separately for women and men. Men

reacted significantly stronger to the second induction; F(2, 38) = 6.024, p < .01 for positive

emotions, F(2, 38) = 8.138, p < .01 for negative emotions. For women the effects of the second

induction were not significant; F(2, 27) = 2.510, n.s. for positive emotions, F(2, 27) = 2.552,

n.s. for negative emotions. As no gender differences were found for the first induction, this

result might be due to the fact that women were less involved in the scenario. A comparison of


means on the emotion scale interest yielded no significant differences, but there was a tendency

for reported means for women to gradually decrease whereas men’s interest remained stable.

To summarise, treatment effects were clearly demonstrated for the second emotion

induction. The effectiveness of the first induction with regard to negative emotions was less

clear than its effects on positive emotions. The effect sizes clearly show that there was a notable

effect on participants’ shame and contentment in both treatment groups.

Do emotions influence the outcome of complex problem solving?

To test the assumption that positive emotions facilitate and negative emotions impair

CPS (Hypothesis 1) under consideration of possible gender effects, a two-way ANOVA was

carried out on scenario performance with feedback and gender. The analysis showed that there

were no effects of emotions on the SP-scale (scenario performance), F(2, 68) < 1, n.s., f = 0.05.

However, a significant main effect of gender on scenario performance was detected, F(1, 68) =

4.763, p < .05. Male students performed better (M = 67.68, SD = 3.42) than did females (M =

56.23, SD = 3.98), d = 1.42.

Creative vs. careful processing: Do positive and negative emotions direct our strategies?

We tested whether positive and negative emotions lead to different information-

processing modes, as specified in Hypothesis 2. A MANOVA was computed on QM-scales

(quality of measures) and AI-scales (acquisition of information) with feedback as a factor.

Emotions indeed had an influence on the AI-scales that was significant for continuity in

information; F(2, 71) = 3.596, p < .05, f = 0.31. There was also a substantial effect for early

orientation in the scenario; F(2, 71) = 2.993, p = .05, f = 0.29. Participants with induced

negative emotions were more thorough in searching and using information as opposed to both

the control group and the positive treatment group. There were differences on the QM-scales

pointing to a better action handling in the group of positively induced participants but not

significantly so. The F-statistic for the multivariate test (Wilk’s lambda) was significant; F(14,

130) = 2.108, p < .05.

Control beliefs: Does trust in one’s own abilities lead to better performance?

In order to test the Hypothesis 3 that an internal LOC leads to a better performance in

CPS than an external LOC, a general linear model (GLM) on scenario performance with the

tertiary scale of the FKK-PS as an independent variable was carried out. Control beliefs had a

small effect (f2 = 0.03) on CPS. This effect was not significant, F(1, 72) = 2.024 – a finding


which might, in fact, be due to sample size and power (a post-hoc analysis revealed a power of

1 - b = 0.31).

Do control beliefs moderate the influence of emotions on complex problem solving?

Next, the moderator hypothesis (Hypothesis 4) was addressed. A GLM with treatment

(positive, negative, no treatment), control beliefs (as measured by the tertiary scale of the FKK-

PS), and the interaction between feedback and control beliefs revealed a significant interaction

between emotion and control beliefs; F(2, 68) = 3.390, p < .05. The effect can be considered as

medium sized with f2 = 0.15.

Figure 2. Interaction between treatment (positive, negative, or control group) and control beliefs(internal or external), showing that control beliefs moderate the influence of emotions on scenarioperformance.

A median split was done on the tertiary scale for illustration purposes to demonstrate the

interaction between feedback and control beliefs. The results of this analysis are depicted in

Figure 2. It can be seen that participants with an internal LOC performed best after receiving an

achievement feedback, especially if this was a positive feedback. They were also better able to

regulate negative emotions. However, participants with an external LOC performed best when

no emotion was induced. These results suggest that the feedback might have had a motivating

side effect.


To take possible gender differences into consideration, separate GLMs by gender were

computed. Surprisingly, the interaction between feedback and control beliefs existed for men,

F(2, 36) = 5.750, p < .01, but not for women, F(2, 26) < 1, n.s.. To summarise, control beliefs

can indeed be seen as a moderator variable that contributes to the relation between emotions

and CPS. The role of gender and the feedback’s motivating effect, which might, in fact, be

responsible for the significance of the interaction, will be discussed below.

DISCUSSION

How did Andrew Wiles so successfully prove Fermat’s last theorem apart from being a

mathematical genius? The answer to that question is not clear based upon the results presented

here, since his task was different from the scenario undertaken by our subjects. However, our

results clearly demonstrate that emotions influence complex problem-solving behaviour,

although they do not exert an influence on overall scenario success unless control beliefs are

considered as a moderator variable.

Emotions do not influence complex problem solving

Concerning the influence of positive and negative emotions on scenario performance

(Hypothesis 1), there were no significant differences between participants that were induced

with positive emotions and those students that received a negative treatment. This result is

inconsistent with most previous studies on simple problem solving (e.g., Isen, 2001). To

account for the finding, we will concentrate on two possible causes: (a) the conceivable

inefficiency of the treatment, and (b) the nature and demands of the complex problem-solving

situation.

Firstly, the result could originate from an inefficient treatment, but the manipulation

check demonstrated that both treatments elicited emotions, and that there were medium to large

effects on the emotion dimensions. Three other issues are related to the question of treatment

efficiency: (a) the intensity and duration of evoked emotions, (b) the influence of labelling state

emotions, and (c) the attention that individuals pay to affective cues and the perceived

relevance of emotions. Considering the first point, differences in emotions were only significant

directly after the treatment was given. Apparently, induced emotions have been intense but

short and did not last long enough to cause substantial differences in scenario performance. As

to the second point, there is some evidence that labelling state emotions can reduce their impact


on cognitive processes (Keltner, Locke, & Audrain, 1993). Emotions have been measured

seven times throughout the experiment in order to gain information about the duration of the

treatment effects and the stability of emotions. Based on the findings of Keltner et al. (1993),

we do assume that the number of emotion questionnaires applied in this study could have

caused an unwanted reduction of emotion influence on measurable cognitive processes.

Concerning attention to emotions, Gasper and Clore (2000) found that the perception of the

relevance of affective cues influences the use of emotions as a basis for judgement. According

to the affect-as-information hypothesis (Schwarz, 1990), affect influences cognitive processes

only when it is experienced as a source of relevant information. We presume here that

participants valued emotional states as relevant, because emotions were induced by

performance feedback directly relevant to the task at hand. Yet, we did not control whether and

how often individuals actually attended to their emotions and can thus only suspect that some

students might have rarely noticed their feelings, let alone considered their feelings as a useful

source of information.

Three conclusions can be drawn from these considerations. First, other emotion

elicitation methods (i.e., inducement by music, pictures, or odours) might well prove to be more

efficient. The music method, however, is only feasible when it does not interfere with the

requirements of the task, as would have been the case here. Alternatively, performance

feedback could have been applied after each cycle. Problematic as this might be, a sustained

treatment could also prohibit effects of scenario-inherent feedback (such as graphical process

information) on students’ performance. Second, the efficiency and duration of the treatment

should be tested beforehand and questionnaires should be omitted in the main experiment to

prevent a possible reduction of effects. Third, a variety of measures have been developed to

assess individual differences in emotional attention and monitoring (for an overview see Gasper

& Clore, 2000; Otto, Döring-Seipel, Grebe & Lantermann, 2001). We think it advisable to

measure emotional attention and clarity as well as emotional stability as traits, especially in a

demanding complex problem-solving situation.

This leads us to a second possible explanation for the findings to Hypothesis 1 that

relates to the nature of the task employed here. The computer-simulated scenario FSYS

constitutes a highly complex, challenging and intriguing situation for the problem solver.


Students might have felt required to regulate their emotions or have been focussing more on the

cognitive task than on their feelings.

Individuals with negative emotions are well-informed problem solvers

Concerning Hypothesis 2, it was shown that positive and negative emotions elicited

distinguishable problem-solving strategies: As predicted by Fiedler’s (2001) affect-cognition

model, negative emotions led to a more detailed information search and to a more systematic

approach to the scenario. The differences on the scale for early orientation in the first five

months can be assumed to be a direct cause of the first treatment. As far as the effect of

treatment on complex problem-solving behaviour is concerned, it can be concluded that

Fiedler’s model applies well to emotions in CPS.

The results of Hypothesis 2 also show that the treatment has been successful. It can be

inferred that emotions do, in fact, influence CPS on the levels of action handling and

information acquisition. One possible conclusion is that different emotion-induced strategies for

CPS lead to the same result in the scenario FSYS. On the one hand, the problem solver has to

be well informed and can thus benefit from negative emotions. On the other hand, quick and

flexible decision-making is required – an approach that is basically elicited by positive emotion.

It is, in fact, probably the switching between different strategies (elicited by both, positive and

negative emotions) that accounts for success in complex problem solving. From this assumption

we draw the conclusion that the dichotomy between positive and negative emotions does not

necessarily lead to a classification of successful and unsuccessful problem solvers in a complex,

dynamic scenario.

Control beliefs do only play a marginal role in complex problem solving

Concerning the role of control beliefs (Hypothesis 3), our aim was twofold: It was

hypothesised that individuals with an internal LOC should be better problem solvers than those

participants with an external LOC. Astonishingly, there were no such differences between

problem solvers with internality versus externality in control beliefs, at least not significantly

so. However, a small effect was found. This finding is consistent with the results yielded from

previous work (e.g., Stäudel, 1987), stating that control beliefs play a marginal role in CPS.

Yet, control beliefs appear to moderate the effect of emotions on CPS, as the results to

Hypothesis 4 indicate: Participants with internal control beliefs benefited most from positive

emotions. Individuals with external control beliefs, however, did not perform best when


negative emotions were elicited, but when no emotions were induced. In contrast to our

expectations, we did not find significant differences between the two treatment groups and the

control group, but between those students who received a feedback and the control group.

Considering the theoretical background of the construct of control beliefs (e.g., Krampen,

1991), we claim that the feedback had a motivating effect on those students with an internal

LOC and that the interaction between treatment and control beliefs is thus due to the

motivational effects of the feedback. Being more active and achievement oriented, individuals

with internal control beliefs can be assumed to seek other people’s feedback on certain tasks

that are relevant to them. In contrast what is suggested by previous work, we conclude that

individuals with an internal orientation in control beliefs depend on external acknowledgement

of their performance to be successful. Participants with an external LOC, on the other hand,

might have mistrusted the positive performance feedback since they hold luck or other people

responsible for their own success or failure.

The role of gender: Men and women perform significantly differently

Although there is evidence in the literature for gender differences in cognitive tasks, we

did not expect to find any differences here since FSYS was formerly believed to be a gender-

neutral scenario (Wagener, 2001). In addition to the main effect of gender on scenario

performance, the moderator effect of control beliefs only holds for men: For women, there was

no significant interaction between control beliefs and emotions. The wide difference between

both groups with regard to self-reported positive and negative emotions was also astonishing,

especially since the literature does, succinctly, not indicate any consistent gender differences in

emotionality (Brody & Hall, 2000). The results of the manipulation check show men to be more

reactive to the emotion induction, especially to the second treatment. Furthermore, men seemed

to be more involved in the scenario, as the comparison of means on the subscale interest

indicates. Women, on the other hand, were less reactive to the feedback. How can we account

for these gender differences? The differences in reactivity to the feedback might simply be due

to the fact that women were more emotionally stable or less in need for a feedback. However, it

is more likely that women were less involved in the scenario and consequently less interested in

their personal success and therefore less exerted.

OUTLOOK


The findings of the present study have interesting implications on both empirical and

theoretical levels. Many aspects of the results demonstrate that studying emotions within the

research domain of CPS requires a different approach from analysing the effects of induced

emotions on simple cognitive processes. Future research should therefore concentrate on (a) the

formation and effect of different problem-solving strategies on behaviour in complex tasks

rather than on emotion-influences on overall success, (b) the moderating influence of traits,

e.g., control beliefs or emotional intelligence, on complex problem solving. Considering the

attention to, clarity, and repair of emotions as moderating trait aspects (Otto et al., 2001) might

enhance the understanding of emotion influences on cognitive processes in complex and

dynamic situations. Moreover, as the affect-cognition link is not unidirectional, we can assume

that perceived success or failure in FSYS might have triggered emotions at any time throughout

the scenario. Either, emotions have to be induced each cycle or post-cognitive emotions have to

be controlled by means of process-tracing methods (thus testing the assumed relations in Figure

1). Several attempts have already been made to compare precise emotions of the same valence

(e.g., Lerner & Keltner, 2000) and further endeavour should be made to induce emotions

purposefully (such as shame and pride by using performance feedback).

It also seems worth to consider extending other theoretical approaches from cognitive

and social psychology to CPS. Mellers has offered a decision-affect theory of anticipated

emotions that takes the emotional responses to the outcome of choice into account (e.g.,

Mellers, Schwartz, & Ritov, 1999). Decision-affect theory provides a descriptive account of

actual emotions and would thus allow us to examine connections between anticipated emotions,

strategy selection in CPS, and actual emotional responses. It might also be possible to

differentiate between distinct emotions in order to answer the normative question whether

people should actually employ emotions as a relevant parameter to guide their behaviour in

complex situations.


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Author Note

Miriam Spering, Institute of Psychology, Ruprecht-Karls-Universität Heidelberg; Dietrich

Wagener, Institute of Psychology, Universität Mannheim; Joachim Funke, Institute of

Psychology, Ruprecht-Karls-Universität Heidelberg.

Correspondence concerning this article should be addressed to Miriam Spering, Institute

of Psychology, Hauptstr. 47-51, 69117 Heidelberg. E-mail: [email protected]

heidelberg.de

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